WithinUsAI/gemini_3.5_flash_distilled_25k

Gemini 3.5 Flash Distilled Dataset (25k)

A 25,000-sample synthetic distilled dataset designed to replicate the core capabilities of Gemini 3.5 Flash: frontier-level agentic execution, rapid multi-step reasoning, dense context analysis, and advanced autonomous coding — all optimized for low-latency inference.

Dataset Summary

This dataset was created via template-based evolutionary synthesis with content-normalized SHA-256 deduplication. Every sample features explicit <thought>-block reasoning, structured outputs, and high-density token distributions — mirroring Gemini 3.5 Flash's thinking-level control and sub-agent execution style.

Dataset Structure

JSONL Format

Each line is a JSON object with three fields:

{
  "instruction": "Design a highly optimized, recursive state-machine pattern for application block 104...",
  "output": "<thought>\n1. Establish immutable state tracking boundaries...\n</thought>\n\nclass StateEngineBlock104:\n    ...",
  "metadata": {
    "category": "agentic_code_synthesis",
    "index": 104
  }
}

Data Fields

Features

• —Structured reasoning: Every response contains an explicit <thought> block with numbered reasoning steps before the final answer.
• —Zero duplicates: Content-normalized SHA-256 hashing ensures no exact or near-identical entries.
• —Token-dense: No filler text — every sample contains high-density logic, code, or structured data.
• —File size: 52.3 MB uncompressed (~12 MB with gzip).

Dataset Creation

Methodology

1. 1.High-cardinality template pools: Per-category generators draw from pools of 100-1000+ variable values (components, algorithms, languages, protocols, metrics, etc.), producing millions of unique prompt combinations.
2. 2.Content-normalized deduplication: Every generated entry is hashed after lowercasing, whitespace removal, and punctuation stripping. Duplicates are skipped.
3. 3.Streaming output: Entries are written directly to JSONL — no in-memory accumulation.
4. 4.Deterministic seed: random.seed(42) for reproducibility.

Deduplication Guarantee

Requirements for Training

pip install datasets

from datasets import load_dataset

dataset = load_dataset("json", data_files="gemini_35_flash_distilled_25k.jsonl")
print(dataset)
# DatasetDict({
#     train: Dataset({
#         features: ['instruction', 'output', 'metadata'],
#         num_rows: 25000
#     })
# })

Intended Use

This dataset is intended for supervised fine-tuning (SFT) and distillation of large language models to produce efficient sub-agents with:

• —Fast, structured reasoning (<thought> loops)
• —Multi-step tool orchestration
• —Autonomous code generation and debugging
• —Dense context analysis and root cause extraction
• —Multimodal structural mapping (audio-visual temporal analysis)
• —Mathematical computation traces
• —Strict instruction and format adherence

Considerations

• —Synthetic data: All samples are programmatically generated. No real user interactions or proprietary information.
• —Domain coverage: Focused on software engineering, enterprise tool use, mathematical reasoning, multimodal analysis, and structured data tasks.
• —No PII: No personal or sensitive content.